One of the distinguishing factors between supervised learning and unsupervised learning is the existence of ground-truth labels which construct a global constraint over examples. In most of the self-supervised learning methods, ground-truth is replaced with some consistency constraint [1]}. Without a doubt, the performance of any self-supervised method is a function of the power of the used consistency constraint.
We define two types of consistency constraints - exemplar consistency and population consistency.
